Bayesian inverse estimation of urban CO₂ emissions: Results from a synthetic data simulation over Salt Lake City, UT

Keyphrases

• Modeling Data 100%
• Synthetic Data 100%
• Urban Carbon Emissions 100%
• Inverse Estimation 100%
• Bayesian Inverse Problems 100%
• Data Simulation 100%
• High-resolution 66%
• Measurement Site 66%
• Carbon Emission Prediction 66%
• Data Mismatch 66%
• Salt Lake Valley 66%
• Mismatch Error 66%
• Utah 33%
• CO2 Flux 33%
• Point Source 33%
• Global CO2 33%
• Transport Model 33%
• Nighttime 33%
• Model Uncertainty 33%
• Monitoring Sites 33%
• Modeling Framework 33%
• Emission Data 33%
• Correlation Time 33%
• Carbon Dioxide (CO2) Emissions 33%
• Posterior Estimates 33%
• Data Relationship 33%
• Model Sensitivity Analysis 33%
• Data-driven Model 33%
• Atmospheric Transport Model 33%
• CO2 Enhancement 33%
• Site Network 33%
• Spatial Correlation 33%
• ODIAC 33%
• Gridded Emission 33%
• Hestia 33%
• Bayesian Inverse Modeling 33%
• Urban Scale 33%
• Priori Estimates 33%
• Spatial Covariance 33%
• Correlation Length 33%
• Temporal Correlation 33%
• Temporal Covariance 33%
• Objective Uncertainty 33%
• Uncertain Structures 33%
• Time-scale Model 33%
• Network Variability 33%
• Prior Uncertainty 33%

Earth and Planetary Sciences

• Emissions 100%
• Carbon Dioxide Emission 100%
• Data Simulation 100%
• Data Product 33%
• Emission Inventory 33%
• Carbon Dioxide 16%
• Utah 16%
• Timescale 16%
• Atmospheric Transport 16%
• Covariance 16%
• Scale Model 16%